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Research on Low-Light Image Enhancement Algorithm Based on Color-Improved Zero-DCE
- 1 Department of Computer Science and Technology, Changchun University of Technology, Changchun, China
* Author to whom correspondence should be addressed.
Abstract
In the field of computer vision, low-light image enhancement is crucial for tasks such as target tracking, detection and entity segmentation. In recent years, although supervised learning-based low-light image enhancement methods have achieved promising results, they still have problems such as relying on a large amount of paired data and high computational cost. To solve these problems, unsupervised methods such as Zero-DCE have emerged, however, through practical observation, the model has bias for color processing. Therefore, in this paper, the color loss function of Zero-DCE model is improved, and experiments are carried out on LOL and VE-LOL-L datasets to compare the performance of the model before and after the improvement, as well as ablation experiments and hyperparameter selection experiments. The results show that the improved model significantly improves the image reconstruction quality and structural similarity, effectively improves the low-light image enhancement effect, and provides a new direction for subsequent related research.
Keywords
Low-light image enhancement, computer vision, deep learning
[1]. Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., ... & De Souza, A. F. (2021). Self-driving cars: A survey. Expert systems with applications, 165, 113816.
[2]. Kim, W. (2022). Low-light image enhancement: A comparative review and prospects. IEEE Access, 10, 84535-84557.
[3]. Ma, L., Ma, T., Liu, R., Fan, X., & Luo, Z. (2022). Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5637-5646).
[4]. Yang, W., Wang, W., Huang, H., Wang, S., & Liu, J. (2021). Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Transactions on Image Processing, 30, 2072-2086.
[5]. Li, J., Feng, X., & Hua, Z. (2021). Low-light image enhancement via progressive-recursive network. IEEE Transactions on Circuits and Systems for Video Technology, 31(11), 4227-4240.
[6]. Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., & Lu, T. (2023, June). Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 3, pp. 2654-2662).
[7]. Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5728-5739).
[8]. Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., ... & Wang, Z. (2021). Enlightengan: Deep light enhancement without paired supervision. IEEE transactions on image processing, 30, 2340-2349.
[9]. Lehr, J., Philipps, J., Hoang, V. N., von Wrangel, D., & Krüger, J. (2021, May). Supervised learning vs. unsupervised learning: A comparison for optical inspection applications in quality control. In IOP Conference Series: Materials Science and Engineering (Vol. 1140, No. 1, p. 012049). IOP Publishing.
[10]. Zheng, S., & Gupta, G. (2022). Semantic-guided zero-shot learning for low-light image/video enhancement. In Proceedings of the IEEE/CVF Winter conference on applications of computer vision (pp. 581-590).
[11]. Li, C., Guo, C., Han, L., Jiang, J., Cheng, M. M., Gu, J., & Loy, C. C. (2021). Low-light image and video enhancement using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(12), 9396-9416.
[12]. Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020). Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1780-1789).
[13]. Unsplash. (n.d.). A platform for high-quality, royalty-free images. https://unsplash.com/. Accessed [2025-01-12].
[14]. Pexels. (n.d.). A website providing free stock photos and videos. https://www.pexels.com/zh-cn/. Accessed [2025-01-12].
[15]. Wei, C., Wang, W., Yang, W., & Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560.
[16]. Liu, J., Xu, D., Yang, W., Fan, M., & Huang, H. (2021). Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision, 129, 1153-1184.
Cite this article
Yu,L. (2025). Research on Low-Light Image Enhancement Algorithm Based on Color-Improved Zero-DCE. Applied and Computational Engineering,142,57-63.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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